Increasing the sales and profit margins is one of the most crucial priority of business owners. Thus, an owner of a local cafe asked me do some analysis on three different data to increase the number of customers which will increase profits.

How could a cafe increases it’s profits?

To answer this question or to discover new information, or confirm an idea they already know, I plan to:

• Discover what are the days that have many customers using time series on visitors data.
• Discover what are the best seller items using the products data.
• Looking for trends to figure out what are the hours, days, and months that have many costumers.

The visit data is from a device the owner of the Cafe put above the main door to count the visitors and have some extra variables.It has 6 variables and 8089 observations.

The items data from the sales website and it has 5 variables and 96 observations.

• The sales data is from the sales website that he is using directly and has the 5 variables and 338 observations.

1. In visit data, the variables are positively skewed due to the large number of zeros and ones since it is hourly data.

2. It is not enough data since the cafe is open for less that a year.

• After uploading the data and exploring the distributions of the variables, I decided to remove the variable Value out since it doesn’t add any value to the number of visitors.

-A large number of zeros is because of the hourly data.

• The time, and Date are the most important variables.

• We can see that there are many customers at night from 7pm until 12pm, but the highest number of visitors were at 22pm and 23pm.

• weekends have the highest number of visitors.

## [1] "Flat white"

## [1] 64124.9

Flat White is the best seller and most profitable item!

## [1] "Flat white"                  "Aqua Carpathica Water 330ml"
## [3] "Hot latte"                   "Cappuccino"

The best selling items are drinks. Looks like we drink hot drinks even in the very hot weather since most of them are hot drinks. (At least water is on the list)

• All the variables are positively skewed. A large number of zeros duo to the closing hours.

• Repeating pattern over time.

• It is a repeating pattern within a year. We can not see that in our data because we don’t have a record for a whole year.

There is no Seasonality in my data.

• Doing a time series model for a short time is not easy and with some challenges such as covid-19 lockdown.
• In the time series, there is not enough data for a whole year, which prevent me from looking to the seasonality. I tried different ways to check it but nothing works.

To increase the number of visitors and thus profits, I suggest focusing on weekends as they tend to increase the number of people. Also, removing unprofitable products from then and increasing the number of profitable products may help.

Deploying the model is one of what I plan to do in the future to feed it with the new data after completing one year from the opening date.

The problem that I faced is that as I said, there is no seasonality in the data. I think that due to covid-19 lockdown and the limited number of customers that are allowed to enter the cafe. The Limitation was in the data since it is for a period that is less than a year.

• Any question?